Explore the innovative Snap Machine Learning (Snap ML) framework in this informative conference talk. Discover how Snap ML combines recent advances in machine learning systems and algorithms to achieve high-performance training of generalized linear models. Learn about its hierarchical architecture designed to reflect modern computing systems and its ability to accelerate training in distributed environments. Gain insights into Snap ML's implementation, including GPU acceleration, pipelining, communication patterns, and software architecture. Examine performance evaluations in single-node and multi-node environments, and understand the benefits of its hierarchical scheme and data streaming functionality. Compare Snap ML's capabilities with popular machine learning software frameworks and explore its impressive performance on a terabyte-scale click-through-rate prediction dataset.